Bibliographic record
Abstract
<p>The term 3D knitting is used in popular media and marketing to describe textile products knit in a single, shaped piece, without seams. 3D knit products provide: increased comfort in wearable items, greater product integrity, new design possibilities, reduction in manual labour, and new opportunities to integrate “smart” fibres. Due to the complex machinery and software used for this type of fabrication, designers engaging with 3D knitting require a different mindset compared to traditional cut and sew or knit design processes. This research was framed by the question: What role do designers play in the current 3D knit ecosystem? A secondary question asked: What are the opportunities and challenges in expanding designers’ skills for 3D knitting? Qualitative inquiry was used to examine designers’ experience in the 3D knit ecosystem in Canada and the United States from the perspectives of three primary stakeholders: designers, software technicians (programmers), and production managers (manufacturers). Modified touchstone tours including semi-structured interviews were conducted with participants (n=18) from the three key stakeholder groups at companies (n=10) across Canada and the United States. Thematic analysis was used to identify and organize key themes from the data. Results indicate that 3D knitting provides benefits in five categories: consumer/product, manufacturer, design and development, business strategy, and sustainability. Challenges and roadblocks were identified in four categories for all stakeholders: costs, education and employment, design and development, and communication. Challenges identified by one stakeholder group were frequently mirrored by or connected to the experience of another group. Results suggest that tacit knowledge contributes to the communication bottleneck in the 3D knit ecosystem. As access to 3D knitting increases, designers’ responsibilities and the scope of their considerations in the design and development process must expand. Designers who understand the goals and priorities of the other stakeholder groups are better equipped to successfully navigate the 3D knit design and development process. This research has implications for current practices in textile and apparel production as well as for higher education institutions preparing design students for careers in this evolving industry.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".